4 citations · 4 across the 4 of their papers we have counts for
4 papers
Training Implicit Generative Models via an Invariant Statistical Loss
José Manuel de Frutos, Pablo M. Olmos, Manuel A. Vázquez +1
Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated…
Maximum likelihood inference for a class of discrete-time Markov-switching time series models with multiple delays
José A. Martínez-Ordóñez, Javier López-Santiago, Joaquín Miguez
Autoregressive Markov switching (ARMS) time series models are used to represent real-world signals whose dynamics may change over time. They have found application in many areas of…
Automatic tempered posterior distributions for Bayesian inversion problems
L. Martino, F. Llorente, E. Curbelo +2
We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. Mo…
A comparison of nonlinear population Monte Carlo and particle Markov chain Monte Carlo algorithms for Bayesian inference in stochastic kinetic models
Eugenia Koblents, Joaquín Míguez
In this paper we address the problem of Monte Carlo approximation of posterior probability distributions in stochastic kinetic models (SKMs). SKMs are multivariate Markov jump proc…